Home Field Advantage in English Premier League

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Playing a match on a team’s home field and hometown provide a number of advantages over a visiting team, including home fans, playing on your own turf, and things as simple as having your own trainers. We aimed to quantify Home Field Advantage (HFA) in the English Premier League (EPL), as determined by goal differential. Additionally, we explored one factor that could of impact HFA; attendance (i.e. crowd size). The data set includes match result and fan attendance from three EPL seasons, 2016-2017, 2017-2018, and 2018-2019. …Read More

Predicting Winners in Cricket: Introducing Sports Analytics to One of the World’s Most-Watched Sports

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The rise in sports analytics has focused on popular American games such as baseball and basketball, as well as soccer. While cricket is one of the most popular sports on a global level, there has been limited application of sport analytics reported in the literature. In this work, established sports analytics methods are applied to cricket; two cricket models are created, a Pythagorean win rate, and an Elo rating system. The cricket Pythagorean win rate was built from One Day Internationals (ODI) results; the runs scored and the runs conceded on a per-year basis for each team, for data spanning the years 2000 to 2021. A limitation of a Pythagorean win rate is that it does not account for the quality of the competing team, and hence an ELO model was also created. The ELO model was built from International Cricket Council (ICC) full members results in ODIs. The Elo model was initialized with ratings = 1500 at the start of the first year, the model then calculated the ratings for all 27 teams in the dataset. Plotting the change over time made it obvious that we should only look at the full members and remove most of the associate members since there are very few data points for the latter. While to date cricket has not followed the movement towards data analytics seen in other major sports, our work shows there are no inherent limitations in cricket data. Both models created are decently robust, and we hope that they will serve as a foundation for others to build on. We hope this works starts a conversation about analytics in cricket.…Read More

Optimal Pitch: Spin Rate & Velocity to Maximize Whiff Rate

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In baseball it is commonly believed that throwing harder, pitches of higher velocity, leads to more whiffs, a batter’s swing and miss at a pitch. In this study, we investigate if pitch spin rate and velocity affect the whiff rate. MLB pitcher pitch data was from baseball savant’s statcast. However, we observe a weak correlation between velocity and whiff rate. If more velocity doesn’t lead to more whiffs, then is spin the key factor that affects a pitcher’s ability to miss bats?…Read More

Quantifying NBA play style drift using Finite Mixture Multinomial model

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NBA games have been changing. In particular, such play style shift, through ’positionless’ basketball, has often been associated with more three- pointer attempts and fewer inefficient mid-range shots. However, while such aggregate trend is trivial to analyze, we pose three outstanding questions that the aggregated trend fails to encapsulate. First, is that there is a growing volume of three-point specialists coming into the league. Second, is that there is a change in play style across all players. Third, is that such trend is heterogeneous across top and bottom performing teams. Through player clusters created from a Finite Mixture Multinomial model, we found that the overall shift towards three-pointers seen in the NBA is attributable both to a growing volume of rookies who are focused on predominantly shooting threes and a more general trend where all-around players are transitioning to become three-point specialist, albeit decreasing over time. In addition, while both the top and bottom performing teams subscribe to similar trends of acquiring more three-point shooters, we do see weak evidence suggesting that top teams appear to be capturing this trend at a faster rate.…Read More

Creating a Better Pythagorean Expectation for Basketball

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Pythagorean win expectation uses the average points scored for and against per game to predict the number of wins a team ‘should’ have won. In this work we attempt to construct a more predictive expectation determination based on per possession points to predict wins totals in NBA games. Additionally, we standardize for seasons and home-court advantage by measuring strength of schedule using per possession numbers. …Read More

Go For It? A Data Driven Analysis of NFL 4th Downs

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While some forward-thinking NFL coaches such as Doug Peterson and John Harbaugh have adopted an analytical approach regarding their fourth down decision making, many have ignored such data. Most NFL coaches do not make consistent data driven 4th down calls. We believe factors that may contribute to this poor decision making are personal image and job security and criticism/risk avoidance. In this study, we explore under what situations it is beneficial for an NFL team to go for it on fourth down rather than settling for a field goal in order to maximize points scored. Our dataset included all 4th down non-kicking plays in the NFL between 2009 -2019. …Read More

Finding the NBA Draft Diamonds in the Rough

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The NBA basketball draft involves a great deal of uncertainty. Some non-lottery picks like Pascal Siakam and Kawhi Leonard go on to have All-Star careers while top draft picks like Kwame Brown, Derrick Williams, and Anthony Bennett end up as busts. In this project, we created a model that uses NBA players standard and advanced stats from college to understand why they were successful or unsuccessful in the NBA. We then apply that model to current college players and try to predict their success in the NBA. Our data set included college and NBA data from Basketball-Reference for every player drafted in the 2007-2016 drafts who played in college and in over 100 games in the NBA. …Read More